class_docstr: MixAugmentationBase base class for customized mix augmentation implementations.
  
  For any augmentation, the implementation of "generate_parameters" and "apply_transform" are required.
  "apply_transform" will need to handle the probabilities internally.
  
  Args:
      p: probability for applying an augmentation. This param controls if to apply the augmentation for the batch.
      p_batch: probability for applying an augmentation to a batch. This param controls the augmentation
        probabilities batch-wise.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input ``True`` or broadcast it
        to the batch form ``False``.
      data_keys: the input type sequential for applying augmentations.
          Accepts "input", "image", "mask", "bbox", "bbox_xyxy", "bbox_xywh", "keypoints".
code: def apply_transform_mask(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
  ) -> Tensor:
      raise NotImplementedError
  
imports: from typing import Any, Dict, List, Optional, Union
  import torch
  from kornia.augmentation.base import _BasicAugmentationBase
  from kornia.augmentation.utils import _transform_input, _transform_output_shape, _validate_input_dtype
  from kornia.constants import DataKey, DType
  from kornia.core import Tensor, tensor
  from kornia.core.check import KORNIA_UNWRAP
  from kornia.geometry.boxes import Boxes
label_desc: Use image node_id 11 for calling the MixAugmentationBaseV2 obj's underlying member method code with example usage: obj.apply_transform_mask(input, params, flags) without return types